/**
 * @file SURF_Homography
 * @brief SURF detector + descriptor + FLANN Matcher + FindHomography
 * @author A. Huaman
 */
#include <jni.h>
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"

using namespace cv;
using namespace std;
/**
 * @function main
 * @brief Main function
 */

extern "C" {

JNIEXPORT void JNICALL Java_edu_calpoly_android_lab1adiel_ImageProcess_FindFeatures(JNIEnv* env, jobject thiz, jlong addrGray, jlong addrRgba ,jlong res)
{

	Mat* pMatGr=(Mat*)addrGray;
	Mat* pMatRgb=(Mat*)addrRgba;
	Mat* pRes = (Mat*)res;

	Mat img_object = *pMatGr;
	Mat img_scene = *pMatRgb;

	if( !img_object.data || !img_scene.data )
	{ std::cout<< " --(!) Error reading images " << std::endl; return; }


	vector<KeyPoint> v;
	FastFeatureDetector detector(50);
	detector.detect(img_object, v);
	for( size_t i = 0; i < v.size(); i++ )
		circle(img_scene, Point(v[i].pt.x, v[i].pt.y), 10, Scalar(255,0,0,255));

	return;



	//-- Step 1: Detect the keypoints using SURF Detector
	int minHessian = 400;
/**
	SurfFeatureDetector detector( minHessian );

	std::vector<KeyPoint> keypoints_object, keypoints_scene;

	detector.detect( img_object, keypoints_object );
	detector.detect( img_scene, keypoints_scene );

	//-- Step 2: Calculate descriptors (feature vectors)
	SurfDescriptorExtractor extractor;

	Mat descriptors_object, descriptors_scene;

	extractor.compute( img_object, keypoints_object, descriptors_object );
	extractor.compute( img_scene, keypoints_scene, descriptors_scene );

	//-- Step 3: Matching descriptor vectors using FLANN matcher
	FlannBasedMatcher matcher;
	std::vector< DMatch > matches;
	matcher.match( descriptors_object, descriptors_scene, matches );

	double max_dist = 0; double min_dist = 100;

	//-- Quick calculation of max and min distances between keypoints
	for( int i = 0; i < descriptors_object.rows; i++ )
	{ double dist = matches[i].distance;
	if( dist < min_dist ) min_dist = dist;
	if( dist > max_dist ) max_dist = dist;
	}

	printf("-- Max dist : %f \n", max_dist );
	printf("-- Min dist : %f \n", min_dist );

	//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
	std::vector< DMatch > good_matches;

	for( int i = 0; i < descriptors_object.rows; i++ )
	{ if( matches[i].distance < 3*min_dist )
	{ good_matches.push_back( matches[i]); }
	}

	Mat img_matches;
	drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
			good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
			vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );


	//-- Localize the object from img_1 in img_2
	std::vector<Point2f> obj;
	std::vector<Point2f> scene;

	for( int i = 0; i < good_matches.size(); i++ )
	{
		//-- Get the keypoints from the good matches
		obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
		scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
	}

	Mat H = findHomography( obj, scene, CV_RANSAC );

	//-- Get the corners from the image_1 ( the object to be "detected" )
	std::vector<Point2f> obj_corners(4);
	obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
	obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
	std::vector<Point2f> scene_corners(4);

	perspectiveTransform( obj_corners, scene_corners, H);


	//-- Draw lines between the corners (the mapped object in the scene - image_2 )
	//line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
	//line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
	//line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
	//line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );

    *pRes =  Mat(img_matches);
	//-- Show detected matches
	//imshow( "Good Matches & Object detection", img_matches );**/
}
}

